AERPAW Find-a-Rover (AFAR) Challenge in December 2023
Data files
Mar 04, 2024 version files 40.54 MB
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AFAR__2023_SigMF.7z
40.53 MB
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README.md
2.36 KB
Sep 08, 2025 version files 68.66 MB
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AFAR__2023_SigMF.zip
68.66 MB
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README.md
4.08 KB
Abstract
Introduction
In December 2023, the AERPAW Find-a-Rover (AFAR) Challenge marked a significant advancement in the field of unmanned aerial and ground vehicle collaboration. The challenge, hosted by AERPAW (Aerial Experimentation and Research Platform for Advanced Wireless), focused on the integration of cutting-edge technologies in unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs).
Objective of the Challenge
The primary objective of the AFAR Challenge was to demonstrate the capability of UAVs in accurately and swiftly localizing a UGV. Competitors were tasked with utilizing a UAV equipped with a software-defined radio (SDR) to detect and localize the UGV. The SDR on the UAV was designed to continuously receive a specific channel-sounding waveform, as detailed in the GE2 example experiment from the AERPAW user manual.
Technical Specifications and Constraints
- Waveform Characteristics: The challenge mandated the use of a narrowband waveform with a bandwidth of 125 KHz. Competitors were restricted from altering the waveform parameters at the UGV, ensuring a standardized test environment.
- Antenna Configuration: The system setup included one transmit antenna and one receiver antenna, with the antenna patterns for both being provided to the participants.
- Environmental Data: Competitors were also given a geographical map of the environment to aid in the strategic deployment of the UAV.
Challenge Execution
Participants in the challenge had the flexibility to either use fixed waypoints for the UAV or develop their own algorithms for trajectory updates. These algorithms could instruct the UAV on the next waypoint to fly to, based on the observed signal strength received from the UGV.
While the experiments have been executed and the data has been collected by the AERPAW Operations team in the real-world testbed environment, the experiments have been originally developed by the participating teams in AERPAW's digital twin. This public dataset includes data for all teams from both the development environment and the real-world environment. Names of the teams, their corresponding institutions, and the names of the team leads, are as follows.
1) Eagles, University of North Texas (Lead: Jaya Sravani Mandapaka)
2) NYU Wireless, NYU (Lead: Weijie Wang)
3) Team SunLab, University of Georgia (Lead: Paul Kudyba)
4) Team Wolfpack, NC State University (Lead: Cole Dickerson)
5) Daedalic Wings, NC State University (Lead: Baisakhi Chatterjee)
https://doi.org/10.5061/dryad.18931zd4g
Description of the data and file structure
Each submission from the competitors was executed three times in the development environment, with variations in the UGV's location for each run. There are two folders: "development" and "testbed." The "development" folder includes data on the received signal power and its quality at three different locations. Specifically, the "power_log" file records the received signal power and the corresponding Unix timestamp as a floating-point number, while the "quality_log" file documents the quality of the received power along with the Unix timestamp.
In the "testbed" folder, along with "power_log" and "quality_log", there is an additional "log" file. This file contains information about the UAV's location, including longitude, latitude, altitude, and the Unix timestamp, providing a comprehensive dataset for each test scenario. The directory also features angles.mat, detailing the UAV’s roll, yaw, and pitch measurements.
Contents
Main folder: AFAR__2023_SigMF.zip
.\development\loc#contains the MATLAB post-processing files (main.mandprocess_txt_CS.m), along with the power and quality log information represented inpower_log.txtandquality_log.txtin TXT format, respectively..\development\loc#\logscontains the power and quality log information in both SigMF and CSV formats..\testbed\loc#, in addition to the files explained in the development folder (main.m,process_txt_CS.m,power_log.txt, andquality_log.txt), contains the function for extracting GPS information. It also contains an additional file,angles.mat, which provides the UAV's roll, yaw, and pitch angles..\testbed\loc#\logscontains the power and quality log information in both SigMF and CSV formats, in addition to the GPS log information in both SigMF and CSV formats.
Raw Data Description
- Power Logs: Each CSV file includes
timestampandpower(in dBm). - GPS Logs: The generated CSV file has 4 columns. Columns from 1 to 4 represent longitude, latitude, altitude (in meters), and timestamp (in seconds from epoch time), respectively.
- Quality Logs: The generated CSV file has 2 columns. The first column represents the
timestamp, while the second column represents the quality of measurement.
Supplementary MATLAB Scripts
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Real_vs_DT.m: This script compares real testbed flight data with digital twin (DT) simulation results.Important: Before running
Real_vs_DT.m, you must first run the main entry script (e.g.,main.m) inside the correspondingtestbed/loc#folder. This step processes the raw logs and generatesresults_all.mat, which is required byReal_vs_DT.m. -
Roll_Yaw_Pitch.m: This standalone script loads theangles.matfile and visualizes the UAV’s roll, pitch, and yaw over time. You can point it to any location (e.g.,328/testbed/loc1) by modifying thefoldervariable.
Presentation File
This presentation (i.e., Datasets from AFAR Challenge.pptx) provides an overview of the AFAR Challenge and datasets. It includes:
- A description of the scenario and dataset structure (real-world and digital twin).
- The role of UAVs and metrics used in evaluation.
- File summaries (e.g.,
log.csv,power_log.txt,quality_log.txt,angles.mat). - Use cases such as A2G modeling, antenna gain analysis, and machine learning applications.
- References to publications using this dataset.
Version changes
8 September 2025: Added angles.mat, which contains the UAV’s roll, yaw, and pitch measurements. Additionally, two new MATLAB scripts have been included: Real_vs_DT.m and Roll_Yaw_Pitch.m. A presentation deck summarizing the AFAR Challenge and the associated datasets has also been added.
SigMF format
This dataset is compatible with SigMF v1.2.0.
Sharing/Access information
NA
Code/Software
Python, MATLAB